Practical Deep Learning at Scale with MLflow

Chapter 1: Deep Learning Life Cycle and MLOps Challenges

The past few years have seen great success in Deep Learning (DL) for solving practical business, industrial, and scientific problems, particularly for tasks such as Natural Language Processing (NLP), image, video, speech recognition, and conversational understanding. While research in these areas has made giant leaps, bringing these DL models from offline experimentation to production and continuously improving the models to deliver sustainable values is still a challenge. For example, a recent article by VentureBeat (https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/) found that 87% of data science projects never make it to production. ...